Shared energy storage configuration method and apparatus for micro-energy network, and storage medium

Abstract

Disclosed in the present invention are a shared energy storage configuration method and apparatus for a micro-energy network, and a storage medium. The method comprises: acquiring basic data of a micro-energy network and a shared energy storage system; on the basis of the basic data, constructing a double-layer optimization model, which comprises an inner-layer optimization model and an outer-layer optimization model; on the basis of the inner-layer optimization model, determining an inner-layer optimization problem to be minimizing the annual operating cost under a given shared energy storage configuration; on the basis of the outer-layer optimization model, determining an outer-layer optimization problem to be maximizing the accommodation rate of photovoltaic power generation; and on the basis of the inner-layer optimization problem and the outer-layer optimization problem, solving the double-layer optimization model, so as to obtain an energy storage configuration optimization scheme. By means of constructing a double-layer optimization model, the embodiments of the present invention optimize the operation of a micro-energy network and a shared energy storage system, such that the photovoltaic accommodation rate can be effectively improved, and the overall cost can be reduced. Therefore, a better energy storage configuration optimization scheme can be obtained by means of solving, and the energy storage optimization effect implemented on the basis of the energy storage configuration optimization scheme can thus be effectively improved.

Description

A method, device and storage medium for configuring shared energy storage in a microgrid Technical Field

[0001] This invention relates to the field of energy storage configuration technology, and in particular to a method, device and storage medium for configuring shared energy storage in a microgrid. Background Technology

[0002] With increasing global focus on renewable energy and my country's goals of achieving "carbon peaking" and "carbon neutrality," the development and utilization of new energy sources have become crucial. In the field of new energy, due to the numerous advantages of solar energy, photovoltaic (PV) technology research is gradually becoming a very important area. PV systems are mainly divided into centralized and distributed types. Distributed PV power generation refers to power generation facilities built near users, characterized by users being able to consume their own power and feed surplus electricity back into the grid, achieving supply and demand balance in the distribution network. This power generation method prioritizes local use, cleanliness, and efficiency, selecting specific methods based on specific circumstances to maximize the absorption of distributed PV, thereby increasing the use of new energy and reducing dependence on traditional fossil fuels. Currently, optimizing energy storage configuration is commonly used to maximize the absorption of distributed PV. With strong national support and promotion of clean energy, the installed capacity of distributed PV is growing rapidly, making the optimization of energy storage configurations for distributed PV and shared energy storage systems increasingly important.

[0003] Existing methods for configuring shared energy storage in microgrids typically do not fully consider the factors affecting energy storage configuration optimization, resulting in poor energy storage configuration performance. Summary of the Invention

[0004] This invention provides a method, apparatus, and storage medium for configuring shared energy storage in microgrids, in order to solve the technical problem that existing methods for configuring shared energy storage in microgrids typically do not fully consider the influencing factors of energy storage configuration optimization, resulting in poor energy storage configuration performance.

[0005] This invention provides a method for configuring shared energy storage in a microgrid, comprising:

[0006] Acquire basic data on microgrids and shared energy storage systems, including photovoltaic power generation, load demand, and energy storage system capacity;

[0007] Based on the aforementioned fundamental data, a two-layer optimization model is constructed, comprising an inner-layer optimization model and an outer-layer optimization model. The first objective function of the inner-layer optimization model is to minimize the annual operating cost of the microgrid, and the first constraints of the inner-layer optimization model include constraints on the microgrid power supply system and the microgrid cooling and heating supply. The second objective function of the outer-layer optimization model is to minimize the overall cost of the shared energy storage system and the microgrid, and the second constraints of the outer-layer optimization model include energy rate constraints, charge / discharge constraints, and energy storage battery state of charge constraints.

[0008] Based on the inner-layer optimization model, the inner-layer optimization problem is determined to be minimizing the annual operating cost under a given shared energy storage configuration; based on the outer-layer optimization model, the outer-layer optimization problem is determined to be maximizing the photovoltaic power generation absorption rate.

[0009] The energy storage configuration optimization scheme is obtained by solving the two-layer optimization model based on the inner-layer optimization problem and the outer-layer optimization problem.

[0010] Furthermore, the first objective function for constructing the inner-layer optimization model to minimize the annual operating cost of the microgrid includes:

[0011] Based on the revenue obtained by the microgrid from selling electricity to the shared energy storage system, the cost of the microgrid purchasing electricity from the shared energy storage system, and the service fees paid by the microgrid, a first objective function is constructed with the goal of minimizing the annual operating cost of the microgrid.

[0012] Furthermore, the second objective function for constructing the outer optimization model by minimizing the overall cost of the shared energy storage system and the microgrid includes:

[0013] Based on the investment cost of the shared energy storage system, the cost of the microgrid purchasing electricity from external sources, and the cost of purchasing fuel for the energy grid, a second objective function is constructed with the goal of minimizing the overall cost of the shared energy storage system and the microgrid.

[0014] Furthermore, the step of solving the two-layer optimization model based on the inner-layer optimization problem and the outer-layer optimization problem to obtain an energy storage configuration optimization scheme includes:

[0015] By using preset KKT conditions, the inner-layer optimization problem is transformed into a third constraint condition for the outer-layer optimization problem. The KKT conditions include gradient conditions, feasibility conditions, and complementary relaxation conditions.

[0016] Based on the penalty number method and the third constraint, the outer-layer optimization problem is solved to obtain the energy storage configuration optimization scheme of the two-layer optimization model.

[0017] Furthermore, the process of solving the outer-layer optimization problem based on the penalty number method and the third constraint, leading to the energy storage configuration optimization scheme of the two-layer optimization model, includes:

[0018] Define a penalty number, and based on the penalty number, convert the data in the bi-level optimization model that does not meet the first constraint, the second constraint, and the third constraint into a penalty term of the second objective function;

[0019] The second objective function is updated according to the penalty term, and the second objective function is solved iteratively for each update. When the two-layer optimization model converges, the optimal energy storage configuration optimization scheme is output.

[0020] The present invention also provides a microgrid shared energy storage configuration device, comprising:

[0021] The basic data acquisition module is used to acquire basic data of micro energy grids and shared energy storage systems, including photovoltaic power generation, load demand and energy storage system capacity.

[0022] A two-layer optimization model construction module is used to construct a two-layer optimization model based on the basic data. The two-layer optimization model includes an inner-layer optimization model and an outer-layer optimization model. The first objective function of the inner-layer optimization model is constructed to minimize the annual operating cost of the microgrid. The first constraints of the inner-layer optimization model include constraints on the microgrid power supply system and constraints on the microgrid cooling and heating supply. The second objective function of the outer-layer optimization model is constructed to minimize the overall cost of the shared energy storage system and the microgrid. The second constraints of the outer-layer optimization model include constraints on energy rate, charge / discharge, and the state of charge of the energy storage battery.

[0023] The optimization problem determination module is used to determine the inner optimization problem as minimizing the annual operating cost under a given shared energy storage configuration, based on the inner optimization model; and to determine the outer optimization problem as maximizing the photovoltaic power generation absorption rate, based on the outer optimization model.

[0024] The dual-layer optimization model solving module is used to solve the dual-layer optimization model based on the inner-layer optimization problem and the outer-layer optimization problem to obtain an energy storage configuration optimization scheme.

[0025] Furthermore, the two-level optimization model building module is also used for:

[0026] Based on the revenue obtained by the microgrid from selling electricity to the shared energy storage system, the cost of the microgrid purchasing electricity from the shared energy storage system, and the service fees paid by the microgrid, a first objective function is constructed with the goal of minimizing the annual operating cost of the microgrid.

[0027] Furthermore, the two-layer optimization model construction module is also used for:

[0028] Based on the investment cost of the shared energy storage system, the cost of the microgrid purchasing electricity from external sources, and the cost of purchasing fuel for the energy grid, a second objective function is constructed with the goal of minimizing the overall cost of the shared energy storage system and the microgrid.

[0029] The present invention also provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the microgrid shared energy storage configuration method as described above.

[0030] The present invention also provides a computer-readable storage medium comprising a stored computer program; wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the microgrid shared energy storage configuration method as described above.

[0031] This invention optimizes the operation of microgrids and shared energy storage systems by constructing a two-layer optimization model. The inner-layer optimization model focuses on the economic operation optimization of the microgrid, aiming to minimize the annual operating cost of the microgrid. The outer-layer optimization model solves the configuration of shared energy storage and the economic absorption of photovoltaic power, which can effectively improve the photovoltaic absorption rate and reduce the overall cost. This allows for the determination of a better energy storage configuration optimization scheme, thereby effectively improving the energy storage optimization effect achieved based on this scheme.

[0032] Furthermore, this embodiment of the invention, through the joint solution of KKT conditions and penalty function method, can obtain the final shared energy storage configuration optimization scheme and micro-energy grid economic dispatch scheme. Moreover, by constructing KKT conditions, the bi-level programming problem can be transformed into a single-level programming problem, which can effectively simplify the complexity of the solution. Furthermore, by introducing the penalty number method, the external penalty mechanism can ensure that all constraints are met, which can effectively improve the solution accuracy. Attached Figure Description

[0033] Figure 1 is a flowchart illustrating the microgrid shared energy storage configuration method provided in an embodiment of the present invention;

[0034] Figure 2 is a schematic diagram of the structure of the microgrid shared energy storage configuration device provided in an embodiment of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0036] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more.

[0037] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0038] Please refer to Figure 1. This invention provides a method for configuring shared energy storage in a microgrid, comprising:

[0039] S1. Obtain basic data on microgrids and shared energy storage systems, including photovoltaic power generation, load demand, and energy storage system capacity.

[0040] In this embodiment of the invention, the microgrid includes distributed photovoltaic power, and its internal equipment configuration includes gas-fired boilers, waste heat boilers, gas turbines, heat exchangers, electric chillers, and absorption chillers. Each microgrid is connected to a shared energy storage system, but there is no direct power exchange.

[0041] S2. Construct a two-layer optimization model based on basic data. The two-layer optimization model includes an inner-layer optimization model and an outer-layer optimization model. The first objective function of the inner-layer optimization model is to minimize the annual operating cost of the microgrid. The first constraints of the inner-layer optimization model include constraints on the microgrid power supply system and the microgrid cooling and heating system. The second objective function of the outer-layer optimization model is to minimize the overall cost of the shared energy storage system and the microgrid. The second constraints of the outer-layer optimization model include constraints on energy rate, charge and discharge, and energy storage battery state of charge.

[0042] S3. Based on the inner-layer optimization model, the inner-layer optimization problem is to minimize the annual operating cost under a given shared energy storage configuration; based on the outer-layer optimization model, the outer-layer optimization problem is to maximize the photovoltaic power generation absorption rate.

[0043] In this embodiment of the invention, the bi-level programming problem can be divided into an outer-level problem and an inner-level problem, where the outer-level problem is the master problem and the inner-level problem is the slave problem. In this embodiment, bi-level programming can optimize the configuration of shared energy storage systems and the scheduling of microgrids within a power system. Specifically, the outer-level optimization problem can be: optimizing the configuration of shared energy storage systems to maximize the absorption rate of photovoltaic power generation while ensuring the overall economic efficiency of the shared energy storage system; the inner-level problem can be: under a given shared energy storage configuration, optimizing the economic operation of the microgrid and minimizing the annual operating cost.

[0044] S4. Solve the two-layer optimization model based on the inner-layer optimization problem and the outer-layer optimization problem to obtain the energy storage configuration optimization scheme.

[0045] This invention optimizes the operation of microgrids and shared energy storage systems by constructing a two-layer optimization model. The inner-layer optimization model focuses on the economic operation optimization of the microgrid, aiming to minimize the annual operating cost of the microgrid. The outer-layer optimization model solves the configuration of shared energy storage and the economic absorption of photovoltaic power, which can effectively improve the photovoltaic absorption rate and reduce the overall cost. This allows for the determination of a better energy storage configuration optimization scheme, thereby effectively improving the energy storage optimization effect achieved based on this scheme.

[0046] In one embodiment, step S2, constructing the first objective function of the inner-layer optimization model to minimize the annual operating cost of the microgrid, includes:

[0047] Based on the revenue obtained by the microgrid from selling electricity to the shared energy storage system, the cost of the microgrid purchasing electricity from the shared energy storage system, and the service fees paid by the microgrid, a first objective function is constructed with the goal of minimizing the annual operating cost of the microgrid.

[0048] In this embodiment of the invention, the first objective function of the inner-layer optimization model can be constructed with the goal of minimizing the annual operating cost of the microgrid: min C MG =C grid +C flue -C ess,s +C ess,b +C serve (1)

[0049] Among them, C ess,s This refers to the revenue that a microgrid receives from selling electricity to a shared energy storage system; C ess,b The cost of purchasing electricity from a shared energy storage system for a microgrid; Cserve This indicates the service fees paid by the micro-energy network.

[0050] Furthermore, the expression for the revenue obtained by a microgrid from selling electricity to a shared energy storage system is as follows:

[0051] in, The revenue from the sale of electricity by the Nth microgrid to the shared energy storage system on typical day M; δ s A matrix of electricity price units sold to the shared energy storage system for each scheduling period; This represents the power matrix of the Nth microgrid selling electricity to the energy storage power station during each scheduling period on the Mth typical day.

[0052] The cost of a microgrid purchasing electricity from a shared energy storage system is expressed as follows:

[0053] in, δ represents the cost of electricity purchased by the Nth microgrid from the shared energy storage system on the Mth typical day; b It is an electricity price matrix that lists the price of purchasing a unit of electricity from the energy storage power station during each dispatch period; It is a power matrix that records the electricity purchased by the Nth microgrid from the energy storage station during each dispatch period on the Mth typical day.

[0054] The expression for the service fee paid by the micro-energy network is as follows:

[0055] in, This represents the service fee paid by microgrid system N to the shared energy storage system on typical day M; δ s It is a service cost matrix used to calculate the unit power service fee paid by the microgrid to the energy storage power station.

[0056] In one embodiment, the microgrid power supply system constraint in the first constraint of the inner optimization model is a constraint that maintains a balance between power supply and demand within the microgrid, as expressed below:

[0057] in, This represents the output power matrix of the photovoltaic system in the Nth microgrid during different scheduling periods on the Mth typical day; These are matrices representing the power consumption of electric chillers and the power of electrical loads during different scheduling periods on a typical day for the microgrid.

[0058] Microgrids can exchange electrical energy with shared energy storage systems, but charging and discharging are not allowed during any given time period. The limitations on their energy exchange are:

[0059] Among them, P ess,max This indicates the maximum permissible exchange power between the microgrid and the shared energy storage system; Let represent the electricity sold to the shared energy storage system and the electricity purchased from the shared energy storage system by the Nth microgrid during the t-schedule period on the Mth typical day, respectively. These are indicator variables, representing whether the Nth microgrid is charging and discharging during scheduling period t.

[0060] The power generation of electrical equipment within a microgrid and the electricity purchased by the microgrid from the main grid must comply with specific restrictions, which can be expressed as:

[0061] Among them, P GT,min P GT,max These represent the lower and upper limits of the power generation capacity of the gas turbine, respectively; P EC,min P EC,max These represent the lower and upper limits of the power consumption of the electric chiller, respectively; P grid,max The maximum power that a microgrid can purchase from the main grid; These represent the output power of the gas turbine and the power consumption of the electric chiller in the Nth microgrid during the t-hour dispatch period on the Mth typical day. The power that microgrid N purchases from the main grid during the dispatch period on typical day M.

[0062] In this embodiment of the invention, the microgrid's cooling and heating constraints are to maintain the power balance of cooling and heating energy, while also satisfying the waste heat balance requirement. The corresponding constraints are expressed as follows:

[0063] in, The matrices represent the heat exchanger thermal power and absorption chiller refrigeration power of the Nth microgrid on the Mth typical day, respectively. η represents the matrices representing the heat load and cooling power of the same microgrid during dispatch period t; HX η AC η WH η EC These represent the efficiency of the heat exchanger, the energy efficiency ratio of the absorption chiller, the efficiency of the waste heat boiler, and the energy efficiency ratio of the chiller, respectively; γ GT This refers to the thermoelectric ratio of a gas turbine.

[0064] The output of cooling and heating systems within a microgrid must also meet certain limitations, the constraints of which are:

[0065] Among them, P HX,min and P HX,max These represent the maximum and minimum power of the heat exchanger, respectively; Q AC,min Q AC,max These are the maximum and minimum power values ​​of the absorption chiller, respectively; Q GB,min Q GB,max These are the upper and lower limits of the power output of gas-fired boilers, respectively. These represent the power of the heat exchanger, the power of the absorption chiller, and the power of the gas boiler of the Nth microgrid during the scheduling period of the Mth typical day t, respectively.

[0066] In this embodiment of the invention, the first constraint also includes a microgrid renewable energy consumption constraint, the expression of which is:

[0067] in, This represents the power generation of the photovoltaic system in the Nth microgrid during the t-schedule period on the Mth typical day; α represents the maximum available photovoltaic resources of the same microgrid during the scheduling period t on typical day M; α is the annual comprehensive absorption rate of new energy in the microgrid; t0 refers to the number of scheduling periods per typical day.

[0068] The embodiments of the present invention can effectively reduce the operating expenses of microgrids by constructing an inner-layer optimization model with the goal of minimizing the annual operating cost of microgrids. Furthermore, the inner-layer optimization model considers the cost of purchasing electricity from shared energy storage systems, the revenue from selling electricity to them, and the service fees paid, enabling microgrids to achieve cost optimization in electricity trading.

[0069] Furthermore, the inner-layer optimization model of this invention, by considering the constraints of power supply, cooling and heating, can effectively optimize the energy management strategy of the microgrid, thereby effectively improving energy utilization efficiency and enhancing the system's responsiveness to demand fluctuations, helping to balance supply and demand, reduce energy waste, and thus effectively improving the effect of energy storage configuration optimization.

[0070] In one embodiment, step S2, constructing a second objective function for the outer optimization model to minimize the overall cost of the shared energy storage system and the microgrid, includes:

[0071] Based on the investment cost of the shared energy storage system, the cost of the microgrid purchasing electricity from external sources, and the cost of purchasing fuel for the energy grid, a second objective function is constructed with the goal of minimizing the overall cost of the shared energy storage system and the microgrid.

[0072] In this embodiment of the invention, the total cost of the shared energy storage system and the microgrid mainly consists of three parts: the investment cost of the shared energy storage power station, the cost of the microgrid purchasing electricity from the external power grid, and the cost of the microgrid purchasing fuel.

[0073] In this embodiment of the invention, the objective function of the outer optimization model, namely the second objective function, is constructed with the goal of minimizing the overall cost of the shared energy storage system and the microgrid. Its expression is as follows: min C = C inv +C grid +C flue (11)

[0074] Among them, C inv C represents the annual value of the investment cost of a shared energy storage system; grid This represents the annual cost of electricity purchased from the grid by a microgrid; C flue This represents the annual fuel purchase cost of the microgrid. By optimizing these cost parameters, embodiments of the present invention can determine the economical operating mode of the shared energy storage power station and the microgrid system, thereby maximizing cost-effectiveness.

[0075] The investment cost of a shared energy storage system includes the initial one-time investment cost amortized annually, and the fixed maintenance expenses payable each year. When assessing the cost of a shared energy storage system, the value of money over time can be factored in, i.e., the time value of money can be calculated. Based on this, the annualized investment cost of a shared energy storage system can be calculated by incorporating the time value of money factor to more accurately reflect the increasing or decreasing present value of the investment over time. The expression for the annualized investment cost is:

[0076] Where r is the annual interest rate; γ is the lifespan of the device; δ P The unit power investment cost; δ E The unit capacity investment cost; δ M The unit power maintenance cost; Pess and Eess are the rated charging and discharging power and rated capacity of the shared energy storage power station, respectively.

[0077] The cost of purchasing electricity from the external grid for a microgrid:

[0078] in, δ0 represents the cost of purchasing electricity from the grid by microgrid N on a typical day M; δ0 represents the grid unit electricity price matrix for each dispatch period. Let N be the power consumption matrix for each scheduling period on typical day M; m and n are the number of typical days and the number of microgrids, respectively.

[0079] The cost of purchasing fuel from the micro-energy network:

[0080] in, c0 represents the cost of purchasing fuel for microgrid N on typical day M; c0 is the cost matrix for natural gas per unit volume. For microgrid N, the power matrix of gas turbines and gas boilers is shown for each scheduling period on typical day M; η GT η GB These represent the efficiency of the gas turbine and the gas boiler, respectively; Q0 ​​represents the calorific value of the gas.

[0081] The energy rate constraint in the second constraint of the outer optimization model stipulates that, in energy storage devices, the storage capacity of a battery must maintain a certain energy rate relationship with its nominal power, expressed as follows: E ess =βP ess (15)

[0082] Where β represents the energy rate of the energy storage battery.

[0083] Regarding charge / discharge constraints, whether a power station charges or discharges within the same dispatch cycle depends on the total energy demand at the bus after all microgrid user power stations have completed energy exchange. Furthermore, shared energy storage systems are not allowed to perform simultaneous charging and discharging within any dispatch cycle; this constraint can be expressed as:

[0084] in, These represent the power sold by microgrid N to the shared energy storage system and the power purchased from the shared energy storage system during the dispatch period t on typical day M, respectively. These represent the charging and discharging power of the shared energy storage system during time period t, respectively. These are indicator variables for the charging and discharging of the shared energy storage system during time period t on the Mth typical day.

[0085] Energy storage battery state of charge constraints:

[0086] in, Let η represent the state of charge of the energy storage battery during scheduling period t on the Mth typical day; abs η relea These represent the charging and discharging efficiencies of the shared energy storage system, respectively. These represent the charging and discharging power of the energy storage power station during dispatch period t on the Mth typical day; k min k max These represent the upper and lower limits of the power plant's state of charge, respectively.

[0087] This invention incorporates the investment cost of the shared energy storage system, the cost of purchasing electricity from external sources for the microgrid, and the cost of purchasing fuel. This enables the two-layer optimization model to effectively reduce the overall cost of the shared energy storage system and the microgrid. Furthermore, the constraints of the outer-layer optimization model, including energy rate constraints, charge / discharge constraints, and energy storage battery state-of-charge constraints, ensure that the energy storage system operates within a safe and efficient range, avoids overcharging and discharging, extends the lifespan of the energy storage system, and keeps the battery state of charge within a reasonable range, thereby improving the optimization effect of energy storage configuration.

[0088] In one embodiment, step S4, solving the two-layer optimization model based on the inner-layer optimization problem and the outer-layer optimization problem to obtain the energy storage configuration optimization scheme, includes:

[0089] S41. By using the preset KKT conditions, the inner-layer optimization problem is transformed into the third constraint condition of the outer-layer optimization problem. The KKT conditions include gradient conditions, feasibility conditions, and complementary relaxation conditions.

[0090] In this embodiment of the invention, the gradient condition is that the partial derivative of the Lagrange function with respect to the variable is equal to zero; the feasibility condition is that the constraint condition must be satisfied; and the complementary relaxation condition is a condition used to handle inequality constraints.

[0091] The embodiments of the present invention can transform the original inner-layer optimization problem into a set of equality constraints and inequality constraints by using preset KKT conditions (Kuhn-Tucker conditions).

[0092] In this embodiment of the invention, the Lagrangian function L(x,λ) of the inner optimization problem can be defined, where x is the decision variable and λ is the Lagrange multiplier. Based on the Lagrangian function, the KKT conditions of the inner optimization problem are constructed according to the constraints of the inner optimization problem.

[0093] Based on the constraints of the inner problem, construct the KKT conditions for the inner problem.

[0094] S42. Based on the penalty number method and the third constraint, solve the outer-layer optimization problem to obtain the energy storage configuration optimization scheme of the two-layer optimization model.

[0095] This invention utilizes KKT conditions to transform the inner-layer optimization problem into constraints for the outer-layer optimization problem. This ensures that the optimal conditions of the inner-layer problem are met when solving the outer-layer optimization problem, effectively coordinating the operating costs of the microgrid and the configuration of the energy storage system, thereby maximizing cost-effectiveness. Furthermore, this invention uses the penalty number method to add penalty terms that do not meet the constraints to the objective function, further ensuring that the energy storage system can reach the optimal operating state, thus effectively improving the optimization effect of energy storage configuration.

[0096] In one embodiment, step S42, based on the penalty number method and the third constraint, solves the outer-layer optimization problem to obtain the energy storage configuration optimization scheme of the two-layer optimization model, including:

[0097] S421. Define a penalty number, and based on the penalty number, convert the data in the bi-level optimization model that does not meet the first, second and third constraints into penalty terms of the second objective function.

[0098] In this embodiment of the invention, the purpose of introducing a penalty term is that if a certain constraint in a two-level optimization model is not satisfied, a larger penalty value is introduced to force the optimization problem to tend to satisfy the constraint in the next iteration.

[0099] The penalty function method can be solved using the following formula:

[0100] Where f(x) is the objective function, g i (x) is the constraint condition, and μ is the penalty parameter, which gradually increases with iteration to force the solution to satisfy the constraint condition.

[0101] S422. Update the second objective function according to the penalty term, and iteratively solve the second objective function for each update. When the two-layer optimization model converges, output the optimal energy storage configuration optimization scheme.

[0102] In this embodiment of the invention, after introducing a penalty function, the transformed single-layer problem can be solved iteratively, including:

[0103] Initially, a small penalty parameter μ is chosen;

[0104] Solve the optimization problem to obtain a temporary solution;

[0105] Gradually increase the penalty parameter μ and repeatedly solve the objective function until the optimal solution that satisfies the constraints is obtained.

[0106] In this embodiment of the invention, during the iteration process, the parameters of the penalty function are adjusted at each step so that the optimal solution gradually satisfies all constraints. After several iterations, the optimal solution of the bilevel programming problem can be gradually approximated by the KKT conditions and the penalty function method, and finally converges to the global optimal solution that satisfies all constraints.

[0107] This invention, through the joint solution of KKT conditions and the penalty function method, can obtain the final optimized scheme for shared energy storage configuration and the economical dispatch scheme for microgrids. Furthermore, by constructing KKT conditions, the bi-level programming problem is transformed into a single-level programming problem, effectively simplifying the solution complexity. Moreover, by introducing the penalty number method, an external penalty mechanism ensures that all constraints are satisfied, effectively improving the solution accuracy. The optimized solution not only satisfies the system's technical constraints (such as voltage and line flow) but also maximizes the photovoltaic power generation absorption rate and reduces the overall operating cost.

[0108] Implementing the embodiments of the present invention has the following beneficial effects:

[0109] This invention optimizes the operation of microgrids and shared energy storage systems by constructing a two-layer optimization model. The inner-layer optimization model focuses on the economic operation optimization of the microgrid, aiming to minimize the annual operating cost of the microgrid. The outer-layer optimization model solves the configuration of shared energy storage and the economic absorption of photovoltaic power, which can effectively improve the photovoltaic absorption rate and reduce the overall cost. This allows for the determination of a better energy storage configuration optimization scheme, thereby effectively improving the energy storage optimization effect achieved based on this scheme.

[0110] Furthermore, this embodiment of the invention, through the joint solution of KKT conditions and penalty function method, can obtain the final shared energy storage configuration optimization scheme and micro-energy grid economic dispatch scheme. Moreover, by constructing KKT conditions, the bi-level programming problem can be transformed into a single-level programming problem, which can effectively simplify the complexity of the solution. Furthermore, by introducing the penalty number method, the external penalty mechanism can ensure that all constraints are met, which can effectively improve the solution accuracy.

[0111] Referring to Figure 2, based on the same inventive concept as the above embodiments, the present invention also provides a microgrid shared energy storage configuration device, comprising:

[0112] The basic data acquisition module 10 is used to acquire basic data of micro energy grids and shared energy storage systems. The basic data includes photovoltaic power generation, load demand and energy storage system capacity.

[0113] The two-layer optimization model construction module 20 is used to construct a two-layer optimization model based on basic data. The two-layer optimization model includes an inner-layer optimization model and an outer-layer optimization model. The first objective function of the inner-layer optimization model is to minimize the annual operating cost of the microgrid. The first constraints of the inner-layer optimization model include constraints on the microgrid power supply system and constraints on the microgrid cooling and heating. The second objective function of the outer-layer optimization model is to minimize the overall cost of the shared energy storage system and the microgrid. The second constraints of the outer-layer optimization model include constraints on energy rate, charge and discharge, and state of charge of the energy storage battery.

[0114] The optimization problem determination module 30 is used to determine the inner optimization problem as minimizing the annual operating cost under a given shared energy storage configuration based on the inner optimization model; and to determine the outer optimization problem as maximizing the photovoltaic power generation absorption rate based on the outer optimization model.

[0115] The two-layer optimization model solving module 40 is used to solve the two-layer optimization model based on the inner-layer optimization problem and the outer-layer optimization problem to obtain the energy storage configuration optimization scheme.

[0116] In one embodiment, the two-layer optimization model building module 20 is further configured to:

[0117] Based on the revenue obtained by the microgrid from selling electricity to the shared energy storage system, the cost of the microgrid purchasing electricity from the shared energy storage system, and the service fees paid by the microgrid, a first objective function is constructed with the goal of minimizing the annual operating cost of the microgrid.

[0118] In one embodiment, the two-layer optimization model building module 20 is further configured to:

[0119] Based on the investment cost of the shared energy storage system, the cost of the microgrid purchasing electricity from external sources, and the cost of purchasing fuel for the energy grid, a second objective function is constructed with the goal of minimizing the overall cost of the shared energy storage system and the microgrid.

[0120] In one embodiment, the two-layer optimization model solving module 40 is further used for:

[0121] By using the pre-defined KKT conditions, the inner-layer optimization problem is transformed into the third constraint of the outer-layer optimization problem. The KKT conditions include gradient conditions, feasibility conditions, and complementary relaxation conditions.

[0122] Based on the penalty number method and the third constraint, the outer-layer optimization problem is solved to obtain the energy storage configuration optimization scheme of the two-layer optimization model.

[0123] In one embodiment, based on the penalty number method and the third constraint, the outer-layer optimization problem is solved to obtain the energy storage configuration optimization scheme of the two-layer optimization model, including:

[0124] Define a penalty number, and based on the penalty number, convert the data in the bi-level optimization model that does not meet the first, second, and third constraints into penalty terms of the second objective function;

[0125] The second objective function is updated based on the penalty term, and the updated second objective function is solved iteratively for each iteration. When the bi-level optimization model converges, the optimal energy storage configuration optimization scheme is output.

[0126] Accordingly, one embodiment of the present invention also provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the microgrid shared energy storage configuration method as described in any of the above embodiments.

[0127] The terminal device of this embodiment includes a processor, a memory, and a computer program and computer instructions stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps in Embodiment 1 above, such as steps S1 to S4 shown in FIG1. ​​Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above device embodiment, such as the optimization problem determination module 30.

[0128] For example, a computer program can be divided into one or more modules / units, one or more of which are stored in memory and executed by a processor to complete the present invention. One or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a terminal device. For example, the optimization problem determination module 30 is used to determine, based on the inner-layer optimization model, that the inner-layer optimization problem is to minimize the annual operating cost under a given shared energy storage configuration; and to determine, based on the outer-layer optimization model, that the outer-layer optimization problem is to maximize the photovoltaic power generation absorption rate.

[0129] Terminal devices can be computing devices such as desktop computers, laptops, handheld computers, and cloud servers. Terminal devices may include, but are not limited to, processors and memory. Those skilled in the art will understand that the schematic diagrams are merely examples of terminal devices and do not constitute a limitation on the terminal devices. They may include more or fewer components than illustrated, or combine certain components, or different components. For example, terminal devices may also include input / output devices, network access devices, buses, etc.

[0130] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device through various interfaces and lines.

[0131] Memory can be used to store computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc.; the data storage area may store data created based on the use of the mobile terminal, etc. In addition, memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0132] If the modules / units integrated into the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0133] Accordingly, one embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the microgrid shared energy storage configuration method as described in any of the above embodiments.

[0134] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention in detail. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A micro energy grid shared energy storage configuration method, characterized in that, include: Acquire basic data on microgrids and shared energy storage systems, including photovoltaic power generation, load demand, and energy storage system capacity; Based on the aforementioned fundamental data, a two-layer optimization model is constructed, comprising an inner-layer optimization model and an outer-layer optimization model. The first objective function of the inner-layer optimization model is to minimize the annual operating cost of the microgrid, and the first constraints of the inner-layer optimization model include constraints on the microgrid power supply system and the microgrid cooling and heating supply. The second objective function of the outer-layer optimization model is to minimize the overall cost of the shared energy storage system and the microgrid, and the second constraints of the outer-layer optimization model include energy rate constraints, charge / discharge constraints, and energy storage battery state of charge constraints. Based on the inner-layer optimization model, the inner-layer optimization problem is determined to be minimizing the annual operating cost under a given shared energy storage configuration; based on the outer-layer optimization model, the outer-layer optimization problem is determined to be maximizing the photovoltaic power generation absorption rate. The energy storage configuration optimization scheme is obtained by solving the two-layer optimization model based on the inner-layer optimization problem and the outer-layer optimization problem.

2. The micro energy grid shared energy storage configuration method of claim 1, wherein, The first objective function for constructing the inner-layer optimization model by minimizing the annual operating cost of the microgrid includes: Based on the revenue obtained by the microgrid from selling electricity to the shared energy storage system, the cost of the microgrid purchasing electricity from the shared energy storage system, and the service fees paid by the microgrid, a first objective function is constructed with the goal of minimizing the annual operating cost of the microgrid.

3. The micro energy grid shared energy storage configuration method of claim 1, wherein, The second objective function of the outer optimization model, which is constructed by minimizing the overall cost of the shared energy storage system and the microgrid, includes: Based on the investment cost of the shared energy storage system, the cost of the microgrid purchasing electricity from external sources, and the cost of purchasing fuel for the energy grid, a second objective function is constructed with the goal of minimizing the overall cost of the shared energy storage system and the microgrid.

4. The micro energy grid shared energy storage configuration method of claim 1, wherein, The process of solving the two-layer optimization model based on the inner-layer optimization problem and the outer-layer optimization problem to obtain an energy storage configuration optimization scheme includes: By using preset KKT conditions, the inner-layer optimization problem is transformed into a third constraint condition for the outer-layer optimization problem. The KKT conditions include gradient conditions, feasibility conditions, and complementary relaxation conditions. Based on the penalty number method and the third constraint, the outer-layer optimization problem is solved to obtain the energy storage configuration optimization scheme of the two-layer optimization model.

5. The micro energy grid shared energy storage configuration method of claim 4, wherein, The method based on the penalty number method and the third constraint, which solves the outer-layer optimization problem to obtain the energy storage configuration optimization scheme of the two-layer optimization model, includes: Define a penalty number, and based on the penalty number, convert the data in the bi-level optimization model that does not meet the first constraint, the second constraint, and the third constraint into a penalty term of the second objective function; The second objective function is updated according to the penalty term, and the second objective function is solved iteratively for each update. When the two-layer optimization model converges, the optimal energy storage configuration optimization scheme is output.

6. A micro energy grid shared energy storage configuration apparatus, characterized in that, include: The basic data acquisition module is used to acquire basic data of micro energy grids and shared energy storage systems, including photovoltaic power generation, load demand and energy storage system capacity. A two-layer optimization model construction module is used to construct a two-layer optimization model based on the basic data. The two-layer optimization model includes an inner-layer optimization model and an outer-layer optimization model. The first objective function of the inner-layer optimization model is constructed to minimize the annual operating cost of the microgrid. The first constraints of the inner-layer optimization model include constraints on the microgrid power supply system and constraints on the microgrid cooling and heating supply. The second objective function of the outer-layer optimization model is constructed to minimize the overall cost of the shared energy storage system and the microgrid. The second constraints of the outer-layer optimization model include constraints on energy rate, charge / discharge, and the state of charge of the energy storage battery. The optimization problem determination module is used to determine the inner optimization problem as minimizing the annual operating cost under a given shared energy storage configuration, based on the inner optimization model; and to determine the outer optimization problem as maximizing the photovoltaic power generation absorption rate, based on the outer optimization model. The dual-layer optimization model solving module is used to solve the dual-layer optimization model based on the inner-layer optimization problem and the outer-layer optimization problem to obtain an energy storage configuration optimization scheme.

7. The micro energy grid shared energy storage configuration apparatus of claim 6, wherein, The two-level optimization model building module is also used for: Based on the revenue obtained by the microgrid from selling electricity to the shared energy storage system, the cost of the microgrid purchasing electricity from the shared energy storage system, and the service fees paid by the microgrid, a first objective function is constructed with the goal of minimizing the annual operating cost of the microgrid.

8. The micro energy grid shared energy storage configuration apparatus of claim 6, wherein, The two-layer optimization model construction module is also used for: Based on the investment cost of the shared energy storage system, the cost of the microgrid purchasing electricity from external sources, and the cost of purchasing fuel for the energy grid, a second objective function is constructed with the goal of minimizing the overall cost of the shared energy storage system and the microgrid.

9. A terminal device, comprising: include: A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the microgrid shared energy storage configuration method as described in any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program; wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the microgrid shared energy storage configuration method as described in any one of claims 1-5.

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